Automatic Extraction of Commonsense LocatedNear Knowledge

LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life. In this paper, we study how to automatically extract such relationship through a sentence-level relation classifier and aggregating the scores of entity pairs from a large corpus. Also, we release two benchmark datasets for evaluation and future research.Comment: Accepted by ACL 2018. A preliminary version is presented on AKBC@NIPS'1

Human Sensation of Transcranial Electric Stimulation.

Noninvasive transcranial electric stimulation is increasingly being used as an advantageous therapy alternative that may activate deep tissues while avoiding drug side-effects. However, not only is there limited evidence for activation of deep tissues by transcranial electric stimulation, its evoked human sensation is understudied and often dismissed as a placebo or secondary effect. By systematically characterizing the human sensation evoked by transcranial alternating-current stimulation, we observed not only stimulus frequency and electrode position dependencies specific for auditory and visual sensation but also a broader presence of somatic sensation ranging from touch and vibration to pain and pressure. We found generally monotonic input-output functions at suprathreshold levels, and often multiple types of sensation occurring simultaneously in response to the same electric stimulation. We further used a recording circuit embedded in a cochlear implant to directly and objectively measure the amount of transcranial electric stimulation reaching the auditory nerve, a deep intercranial target located in the densest bone of the skull. We found an optimal configuration using an ear canal electrode and low-frequency (<300 Hz) sinusoids that delivered maximally ~1% of the transcranial current to the auditory nerve, which was sufficient to produce sound sensation even in deafened ears. Our results suggest that frequency resonance due to neuronal intrinsic electric properties need to be explored for targeted deep brain stimulation and novel brain-computer interfaces

Sulfur isotopic compositions of submicrometer SiC grains from the Murchison meteorite

We report C, Si, N, S, Mg-Al, and Ca-Ti isotopic compositions of presolar silicon carbide (SiC) grains from the SiC-rich KJE size fraction (0.5-0.8 μm) of the Murchison meteorite. One thousand one hundred thirteen SiC grains were identified based on their C and Si isotopic ratios. Mainstream, AB, C, X, Y, and Z subtypes of SiC, and X-type silicon nitride (Si₃N₄) account for 81.4%, 5.7%, 0.1%, 1.5%, 5.8%, 4.9%, and 0.4%, respectively. Twenty-five grains with unusual Si isotopic ratios, including one C grain, 16 X grains, 1 Y grain, 5 Z grains, and 2 X-type Si₃N₄ grains were selected for N, S, Mg-Al, and Ca-Ti isotopic analysis. The C grain is highly enriched in ²⁹Si and ³⁰Si (δ²⁹Si = 1345‰ ± 19‰, δ³⁰Si = 1272‰ ± 19‰). It has a huge ³²S excess, larger than any seen before, and larger than that predicted for the Si/S supernova (SN) zone, providing evidence against the elemental fractionation model by Hoppe et al. Two SN models investigated here present a more satisfying explanation in terms of a radiogenic origin of ³²S from the decay of short-lived ³²Si (τ1/2 = 153 yr). Silicon-32 as well as ²⁹Si and ³⁰Si can be produced in SNe by short neutron bursts; evidence for initial 44Ti (τ1/2 = 60 yr) in the C grain is additional evidence for an SN origin. The X grains have marginal ³²S excesses, much smaller than expected from their large ²⁸Si excesses. Similarly, the Y and Z grains do not show the S-isotopic anomalies expected from their large Si isotopic anomalies. Low intrinsic S contents and contamination with isotopically normal S are the most likely explanations